2 Web MiningWeb mining is the application of data mining techniques to find interesting and potentially useful knowledge from web data.
3 What is Web Data ? Web data is Web content –text,image,records,etc. Web structure –hyperlinks,tags,etc.Web usage –http logs,app server logs,etc.
4 Web Mining Taxonomy Web-Mining Web Usage Mining Web Structured Mining Web Content Mining
5 Web Content MiningDiscovery of useful information from web contents / data / documentsWeb data contents:text,image,audio,video,metadata andhyperlinks
6 Web Structured MiningIt deal with discovering and modeling the link structure of the web.Work has been carried out to model the web based on the topology of the hyperlinks.Helps inDiscovering similarities between sitesIn discovering important sites for a particular topic.Discovering web communities.
7 Web Usage MiningIt deals with understanding user behavior in interacting with the web or with a website.AimTo obtain information that may assist web sites for reorganization or adaptation to better suit the user.
8 To understand user’s behaviour Clicking patternBrowsing timeTransaction
9 Application Target potential customers for electronic commerce Enhance the quality and delivery of Internet information services to the end userImprove Web server system performanceIdentify potential prime advertisement locationsFacilitates personalization/adaptive sitesImprove site designFraud/intrusion detectionPredict user’s actions (allows pre fetching)
10 Web Mining Taxonomy Application Level Logs Web-Mining Web Usage Mining Web Structured MiningWeb Content MiningTextImageAudioVideoStructuredWeb-Server LogsApplication Server LogsDocument StructuredHyperlinksIntra-Document HyperlinksInter-Document Hyperlinks
11 Web usage mining and E-Commerce E-commerce is the killer-application of web miningKeep former customers and attract new customersProvide better service and be more interactiveWeb usage mining is the best way to analyse the customer’s behaviour.Discover customers needs or interestsAnalyse customers behaviour
12 Pattern discovery and analysis The KDD Process for E-commerceActionData collection andPre-processingPattern discovery and analysisReconditionsAgain ActionMining
13 Pattern Discovery and Analysis Using the mining algorithms to discover the patternPattern AnalysisTo filter out uninteresting/meaningless rules or patterns from the set found in the pattern discovery phaseInformation filterOLAP (On-line analytical processing)VisualizationKnowledge query mechanism (SQL)
14 Technologies For Web Usage Mining Web usage mining technologiesStatistical analysisMost common method, such as frequency, mean (average), median, etc.ClassificationMapping a data item into one of several predefined classesClusteringTo group together a set of items having similar characteristics
15 Technologies For Web Usage Mining Association ruleCan be used to relate page or product that are most often referenced or purchased togetherSequential patternsA set of items is followed by another item in time-order
16 E-commerce Business Objectives PersonalizationWeb site personalization (content or layout)Personalized advertisementPersonalized product recommendationMarketing strategyMarketing ruleChanging the marketing strategyWeb site designWeb site evaluationReorganizeImprove the hypertext structureOptimization
17 Web usage mining for e-commerce Many applications in different areas of E-commerce have already been proposedHowever, most research just focuses on the first two steps of the KDD processData mining is meaningless if we do not take action in E-commerce
18 In the area of web usage mining for Possible workIn the area of web usage mining forE-commerce. Also in the area of web searchemploying Web Crawlers or algorithms likeHITS (Hypertext Induced topic search), WebWarehousing etc.